Enhancing Community Well-being Through Public Transport Accessibility¶
- As a public health researcher, I want to analyze the impact of public transport accessibility on the health and well-being of Melbourne residents.
Public transport can significantly influence people's access to health services, social connections, and overall quality of life. This analysis aims to identify how proximity to bus and tram stops correlates with various well-being indicators, such as physical and mental health.
- As a city planner, I want to understand the relationship between public transport access and community participation.
Community participation can be facilitated by convenient public transport options, allowing residents to engage in social activities, work, and leisure. This analysis will help in planning public transport routes to enhance community connectivity.
- Learn how to import and integrate data from multiple sources, including survey data and geospatial data.
- Understand methods for geocoding and calculating distances between locations.
- Gain skills in analyzing and visualizing the relationship between public transport accessibility and community well-being indicators.
- Be able to present findings that can influence public policy and urban planning decisions.
Public transport plays a crucial role in urban settings, offering accessibility and mobility to residents. For a city like Melbourne, which is known for its high quality of life, understanding the role of public transport in enhancing community well-being is vital. This analysis seeks to explore how close proximity to bus and tram stops influences residents' physical health, mental well-being, social connections, and participation in community activities.
Key Factors of Analysis
- Physical Health: How does public transport accessibility impact access to healthcare services and physical activity levels?
- Mental Well-being: Is there a correlation between easy access to public transport and reduced stress or improved mental health?
- Community Participation: Does proximity to public transport encourage participation in social and community activities?
- Social Connectedness: How does public transport influence social interactions and connections?
DATASETS :
Dataset 1: https://data.melbourne.vic.gov.au/explore/dataset/social-indicators-for-city-of-melbourne-residents-2023/information/ Title: Social Indicators for City of Melbourne Residents 2023 (CoMSIS) Source: City of Melbourne Open Data Portal Description: This dataset provides comprehensive social and demographic data for Melbourne residents, including health, well-being, and transport-related information.
Dataset 2: https://data.melbourne.vic.gov.au/explore/dataset/bus-stops/information/ Title: Bus Stops Source: City of Melbourne Open Data Portal Description: This dataset contains the location of bus stops within the city of Melbourne.
Dataset 3: : https://data.melbourne.vic.gov.au/explore/dataset/tram-tracks/export/ Title: Tram Stops Source: City of Melbourne Open Data Portal Description: This dataset contains the location of tram stops within the city of Melbourne.
- PART-1 DOWNLOADING DATASETS
- PART-2 DATA CLEANING
- PART-3 DATA INTEGRATION
- PART-4 EXPLORATIVE DATA ANALYSIS
- Part-5 STATISTICAL AND SPATIAL ANALYSIS
- PART-6 VISUALIZATION AND ANALYSIS OF PUBLIC TRANSPORT ACCESSIBILITY AND ITS IMPACT ON HEALTH OUTCOMES
- Part-7 RECOMMENDATIONS
| indicator | type | topic | description | response | respondent_group | year | sample_size | result | format | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 25-34 years | 2023 | 419 | 17.1 | per cent |
| 1 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 45-54 years | 2023 | 128 | 15.0 | per cent |
| 2 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 65+ years | 2023 | 202 | 3.6 | per cent |
| 3 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | docklands 3008 | 2023 | 113 | 4.5 | per cent |
| 4 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | melbourne 3000 | 2023 | 338 | 18.0 | per cent |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 495 | 6a | other | food security | worried food would run out | yes, in the last 12 months | melbourne 3000 | 2023 | 341 | 25.1 | per cent |
| 496 | 6a | other | food security | worried food would run out | yes, in the last 12 months | parkville 3052 | 2023 | 77 | 20.1 | per cent |
| 497 | 6a | other | food security | worried food would run out | yes, in the last 12 months | south yarra 3141 / melbourne/st kilda road 3004 | 2023 | 138 | 28.2 | per cent |
| 498 | 6b | other | food security | skipped meals | yes, in the last 12 months | 18-24 years | 2023 | 273 | 32.0 | per cent |
| 499 | 6b | other | food security | skipped meals | yes, in the last 12 months | kensington / flemington 3031 | 2023 | 89 | 9.0 | per cent |
500 rows × 10 columns
social_indicators_df.head(500)
| indicator | type | topic | description | response | year | sample_size | result | format | age_group | location | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 419 | 17.1 | per cent | 25-34 years | None |
| 1 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 128 | 15.0 | per cent | 45-54 years | None |
| 2 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 202 | 3.6 | per cent | 65+ years | None |
| 3 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 113 | 4.5 | per cent | None | docklands 3008 |
| 4 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 338 | 18.0 | per cent | None | melbourne 3000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 495 | 6a | other | food security | worried food would run out | yes, in the last 12 months | 2023 | 341 | 25.1 | per cent | None | melbourne 3000 |
| 496 | 6a | other | food security | worried food would run out | yes, in the last 12 months | 2023 | 77 | 20.1 | per cent | None | parkville 3052 |
| 497 | 6a | other | food security | worried food would run out | yes, in the last 12 months | 2023 | 138 | 28.2 | per cent | None | south yarra 3141 / melbourne/st kilda road 3004 |
| 498 | 6b | other | food security | skipped meals | yes, in the last 12 months | 2023 | 273 | 32.0 | per cent | 18-24 years | None |
| 499 | 6b | other | food security | skipped meals | yes, in the last 12 months | 2023 | 89 | 9.0 | per cent | None | kensington / flemington 3031 |
500 rows × 11 columns
social_indicators_df.head(594)
| indicator | type | topic | description | response | year | sample_size | result | format | age_group | location | latitude | longitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 419 | 17.1 | per cent | 25-34 years | None | 44.933143 | 7.540121 |
| 1 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 128 | 15.0 | per cent | 45-54 years | None | 44.933143 | 7.540121 |
| 2 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 202 | 3.6 | per cent | 65+ years | None | 44.933143 | 7.540121 |
| 3 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 113 | 4.5 | per cent | None | docklands 3008 | -37.817542 | 144.939492 |
| 4 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 338 | 18.0 | per cent | None | melbourne 3000 | -37.814245 | 144.963173 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 589 | 9 | other | quality of life | satisfaction with life as a whole | average satisfaction score (from 0-100) | 2023 | 202 | 80.6 | average | 65+ years | None | 44.933143 | 7.540121 |
| 590 | 9 | other | quality of life | satisfaction with life as a whole | average satisfaction score (from 0-100) | 2023 | 192 | 69.3 | average | None | carlton 3053 | -37.800423 | 144.968434 |
| 591 | 9 | other | quality of life | satisfaction with life as a whole | average satisfaction score (from 0-100) | 2023 | 1369 | 72.7 | average | None | city of melbourne | -37.812382 | 144.948265 |
| 592 | 9 | other | quality of life | satisfaction with life as a whole | average satisfaction score (from 0-100) | 2023 | 69 | 78.7 | average | None | east melbourne 3002 | -37.812498 | 144.985885 |
| 593 | 9 | other | quality of life | satisfaction with life as a whole | average satisfaction score (from 0-100) | 2023 | 89 | 74.0 | average | None | kensington / flemington 3031 | -37.788559 | 144.931535 |
594 rows × 13 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 309 entries, 0 to 308 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 geo_point_2d 309 non-null object 1 geo_shape 309 non-null object 2 prop_id 309 non-null int64 3 addresspt1 309 non-null float64 4 addressp_1 309 non-null int64 5 asset_clas 309 non-null object 6 asset_type 309 non-null object 7 objectid 309 non-null int64 8 str_id 309 non-null int64 9 addresspt 309 non-null int64 10 asset_subt 0 non-null float64 11 model_desc 309 non-null object 12 mcc_id 309 non-null int64 13 roadseg_id 309 non-null int64 14 descriptio 309 non-null object 15 model_no 309 non-null object dtypes: float64(2), int64(7), object(7) memory usage: 38.8+ KB
| geo_point_2d | geo_shape | prop_id | addresspt1 | addressp_1 | asset_clas | asset_type | objectid | str_id | addresspt | asset_subt | model_desc | mcc_id | roadseg_id | descriptio | model_no | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -37.80384165792465, 144.93239283833262 | {"coordinates": [144.93239283833262, -37.80384... | 0 | 76.819824 | 357 | Signage | Sign - Public Transport | 355 | 1235255 | 570648 | NaN | Sign - Public Transport 1 Panel | 1235255 | 21673 | Sign - Public Transport 1 Panel Bus Stop Type 13 | P.16 |
| 1 | -37.81548699581418, 144.9581794249902 | {"coordinates": [144.9581794249902, -37.815486... | 0 | 21.561304 | 83 | Signage | Sign - Public Transport | 600 | 1231226 | 548056 | NaN | Sign - Public Transport 1 Panel | 1231226 | 20184 | Sign - Public Transport 1 Panel Bus Stop Type 8 | P.16 |
| 2 | -37.81353897396532, 144.95728334230756 | {"coordinates": [144.95728334230756, -37.81353... | 0 | 42.177187 | 207 | Signage | Sign - Public Transport | 640 | 1237092 | 543382 | NaN | Sign - Public Transport 1 Panel | 1237092 | 20186 | Sign - Public Transport 1 Panel Bus Stop Type 8 | P.16 |
| 3 | -37.82191394843844, 144.95539345270072 | {"coordinates": [144.95539345270072, -37.82191... | 0 | 15.860434 | 181 | Signage | Sign - Public Transport | 918 | 1232777 | 103975 | NaN | Sign - Public Transport 1 Panel | 1232777 | 22174 | Sign - Public Transport 1 Panel Bus Stop Type 8 | P.16 |
| 4 | -37.83316401267591, 144.97443745130263 | {"coordinates": [144.97443745130263, -37.83316... | 0 | 0.000000 | 0 | Signage | Sign - Public Transport | 1029 | 1271914 | 0 | NaN | Sign - Public Transport 1 Panel | 1271914 | 22708 | Sign - Public Transport 1 Panel Bus Stop Type 8 | P.16 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 295 | -37.830076314348155, 144.96531772571083 | {"coordinates": [144.96531772571083, -37.83007... | 0 | 16.382280 | 121 | Signage | Sign - Public Transport | 40427 | 1239220 | 110628 | NaN | Sign - Public Transport 1 Panel | 1239220 | 22118 | Sign - Public Transport 1 Panel Bus Stop Type 8 | P.16 |
| 296 | -37.82097678869638, 144.92581314868238 | {"coordinates": [144.92581314868238, -37.82097... | 0 | 77.355590 | 154 | Signage | Sign - Public Transport | 40450 | 1245195 | 562527 | NaN | Sign - Public Transport 1 Panel | 1245195 | 22156 | Sign - Public Transport 1 Panel Bus Stop Type 3 | P.16 |
| 297 | -37.796717481892664, 144.94652849185758 | {"coordinates": [144.94652849185758, -37.79671... | 0 | 14.595037 | 215 | Signage | Sign - Public Transport | 40643 | 1249762 | 565421 | NaN | Sign - Public Transport 1 Panel | 1249762 | 20907 | Sign - Public Transport 1 Panel Bus Stop Type 8 | P.16 |
| 298 | -37.84536002766068, 144.982312412603 | {"coordinates": [144.982312412603, -37.8453600... | 0 | 0.000000 | 0 | Signage | Sign - Public Transport | 41418 | 1255285 | 0 | NaN | Sign - Public Transport 1 Panel | 1255285 | 22308 | Sign - Public Transport 1 Panel Bus Stop Type 8 | P.16 |
| 299 | -37.80136463912211, 144.91440645303163 | {"coordinates": [144.91440645303163, -37.80136... | 0 | 9.334432 | 19 | Signage | Sign - Public Transport | 41465 | 1463005 | 654920 | NaN | Sign - Public Transport 1 Panel | 1463005 | 21683 | Sign - Public Transport 1 Panel Bus Stop Type 3 | P.16 |
300 rows × 16 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 645 entries, 0 to 644 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 description 645 non-null object 1 name 645 non-null object 2 latitude 645 non-null float64 3 longitude 645 non-null float64 4 geo_shape_lat 645 non-null float64 5 geo_shape_lon 645 non-null float64 dtypes: float64(4), object(2) memory usage: 30.4+ KB None
| description | name | latitude | longitude | geo_shape_lat | geo_shape_lon | |
|---|---|---|---|---|---|---|
| 0 | Attributes< | kml_3 | -37.788613 | 144.934616 | 144.934525 | -37.788621 |
| 1 | Attributes< | kml_5 | -37.819186 | 144.961035 | 144.960994 | -37.819175 |
| 2 | Attributes< | kml_6 | -37.818380 | 144.959453 | 144.959344 | -37.818227 |
| 3 | Attributes< | kml_7 | -37.814404 | 144.970251 | 144.969150 | -37.814700 |
| 4 | Attributes< | kml_8 | -37.816739 | 144.969909 | 144.970083 | -37.816716 |
| ... | ... | ... | ... | ... | ... | ... |
| 640 | Attributes< | kml_622 | -37.811666 | 144.956372 | 144.956422 | -37.811691 |
| 641 | Attributes< | kml_626 | -37.811041 | 144.958897 | 144.959070 | -37.811019 |
| 642 | Attributes< | kml_629 | -37.810688 | 144.960102 | 144.959047 | -37.810969 |
| 643 | Attributes< | kml_641 | -37.832398 | 144.971967 | 144.971857 | -37.832174 |
| 644 | Attributes< | kml_644 | -37.821467 | 144.969274 | 144.969284 | -37.821401 |
645 rows × 6 columns
| indicator | type | topic | description | response | year | sample_size | result | format | age_group | location | latitude | longitude | nearest_bus_stop_distance | nearest_tram_stop_distance | accessibility | bus_stop_travel_time | tram_stop_travel_time | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 419 | 17.1 | per cent | 25-34 years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 |
| 1 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 128 | 15.0 | per cent | 45-54 years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 |
| 2 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 202 | 3.6 | per cent | 65+ years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 |
| 3 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 113 | 4.5 | per cent | None | docklands 3008 | -37.817542 | 144.939492 | 6.446944e+02 | 3.411385e+02 | Very Good | 7.736333e+01 | 4.093662e+01 |
| 4 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 338 | 18.0 | per cent | None | melbourne 3000 | -37.814245 | 144.963173 | 2.291950e+02 | 2.736518e+00 | Very Good | 2.750340e+01 | 3.283822e-01 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 295 | 6 | council plan indicator | food security | experienced food insecurity (worried food woul... | yes, in the last 12 months | 2023 | 89 | 18.1 | per cent | None | kensington / flemington 3031 | -37.788559 | 144.931535 | 1.760548e+02 | 5.733400e+01 | Very Good | 2.112658e+01 | 6.880079e+00 |
| 296 | 6 | council plan indicator | food security | experienced food insecurity (worried food woul... | yes, in the last 12 months | 2023 | 344 | 36.5 | per cent | None | melbourne 3000 | -37.814245 | 144.963173 | 2.291950e+02 | 2.736518e+00 | Very Good | 2.750340e+01 | 3.283822e-01 |
| 297 | 6 | council plan indicator | food security | experienced food insecurity (worried food woul... | yes, in the last 12 months | 2023 | 77 | 29.5 | per cent | None | parkville 3052 | -37.787115 | 144.951553 | 6.527690e+02 | 6.612698e+02 | Very Good | 7.833229e+01 | 7.935238e+01 |
| 298 | 6a | other | food security | worried food would run out | yes, in the last 12 months | 2023 | 420 | 24.4 | per cent | 25-34 years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 |
| 299 | 6a | other | food security | worried food would run out | yes, in the last 12 months | 2023 | 69 | 15.5 | per cent | None | east melbourne 3002 | -37.812498 | 144.985885 | 7.815369e+02 | 3.530502e+02 | Very Good | 9.378442e+01 | 4.236602e+01 |
300 rows × 18 columns
| indicator | type | topic | description | response | year | sample_size | result | format | age_group | location | latitude | longitude | nearest_bus_stop_distance | nearest_tram_stop_distance | accessibility | bus_stop_travel_time | tram_stop_travel_time | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 419 | 17.1 | per cent | 25-34 years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 |
| 1 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 128 | 15.0 | per cent | 45-54 years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 |
| 2 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 202 | 3.6 | per cent | 65+ years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 |
| 3 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 113 | 4.5 | per cent | None | docklands 3008 | -37.817542 | 144.939492 | 6.446944e+02 | 3.411385e+02 | Very Good | 7.736333e+01 | 4.093662e+01 |
| 4 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 338 | 18.0 | per cent | None | melbourne 3000 | -37.814245 | 144.963173 | 2.291950e+02 | 2.736518e+00 | Very Good | 2.750340e+01 | 3.283822e-01 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 295 | 6 | council plan indicator | food security | experienced food insecurity (worried food woul... | yes, in the last 12 months | 2023 | 89 | 18.1 | per cent | None | kensington / flemington 3031 | -37.788559 | 144.931535 | 1.760548e+02 | 5.733400e+01 | Very Good | 2.112658e+01 | 6.880079e+00 |
| 296 | 6 | council plan indicator | food security | experienced food insecurity (worried food woul... | yes, in the last 12 months | 2023 | 344 | 36.5 | per cent | None | melbourne 3000 | -37.814245 | 144.963173 | 2.291950e+02 | 2.736518e+00 | Very Good | 2.750340e+01 | 3.283822e-01 |
| 297 | 6 | council plan indicator | food security | experienced food insecurity (worried food woul... | yes, in the last 12 months | 2023 | 77 | 29.5 | per cent | None | parkville 3052 | -37.787115 | 144.951553 | 6.527690e+02 | 6.612698e+02 | Very Good | 7.833229e+01 | 7.935238e+01 |
| 298 | 6a | other | food security | worried food would run out | yes, in the last 12 months | 2023 | 420 | 24.4 | per cent | 25-34 years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 |
| 299 | 6a | other | food security | worried food would run out | yes, in the last 12 months | 2023 | 69 | 15.5 | per cent | None | east melbourne 3002 | -37.812498 | 144.985885 | 7.815369e+02 | 3.530502e+02 | Very Good | 9.378442e+01 | 4.236602e+01 |
300 rows × 18 columns
DATA VERIFICATION AND QUALITY CHECK(after integration)
Missing values in each column: indicator 0 type 0 topic 0 description 0 response 0 year 0 sample_size 0 result 0 format 0 age_group 396 location 198 latitude 0 longitude 0 nearest_bus_stop_distance 0 nearest_tram_stop_distance 0 accessibility 0 bus_stop_travel_time 0 tram_stop_travel_time 0 dtype: int64
EXPLORATIVE DATA ANALYSIS
The primary goal here is to gain an initial understanding of the data, uncover patterns, and identify relationships between variables that can help guide further analysis.
- Descriptive Statistics for Distance and Travel Time: This section provides basic summary statistics for the columns related to the distance to bus/tram stops and travel times.
- Distribution of Distances to the Nearest Bus Stop:This part includes the visualization of the distribution of distances to the nearest bus stop using a histogram.
- Relationship Between Bus Stop Distance and Well-being Indicator: This section visualizes the relationship between the distance to the nearest bus stop and the well-being indicator using a scatter plot.
- Geographical Map of Respondent Locations: Here, I am visualizing the geographical distribution of respondents using their latitude and longitude data with Folium maps.
- Categorizing Distance to Public Transport:categorizing the distance into classes like "Very Close," "Moderate," and "Far" for both bus and tram stop distances.
- Converting Categorical Data to Numerical Data: Here we use label encoding to convert categorical columns (distance categories) into numerical values for analysis
- Correlation Analysis of Transport Accessibility and Well-being Indicators:In this part, we calculate the correlation matrix between the numeric columns (distances, travel times, and distance categories) and visualize it using a heatmap.
Descriptive statistics:
nearest_bus_stop_distance nearest_tram_stop_distance \
count 5.940000e+02 5.940000e+02
mean 8.576338e+06 8.576792e+06
std 7.786932e+06 7.787432e+06
min 1.247726e+02 2.736518e+00
25% 6.527690e+02 3.530502e+02
50% 1.449454e+07 1.449805e+07
75% 1.641019e+07 1.641037e+07
max 1.693035e+07 1.693056e+07
bus_stop_travel_time tram_stop_travel_time
count 5.940000e+02 5.940000e+02
mean 1.029161e+06 1.029215e+06
std 9.344319e+05 9.344918e+05
min 1.497271e+01 3.283822e-01
25% 7.833229e+01 4.236602e+01
50% 1.739345e+06 1.739766e+06
75% 1.969222e+06 1.969245e+06
max 2.031642e+06 2.031667e+06
latitude 57 longitude 57 location 198 dtype: int64
| indicator | type | topic | description | response | year | sample_size | result | format | age_group | location | latitude | longitude | nearest_bus_stop_distance | nearest_tram_stop_distance | accessibility | bus_stop_travel_time | tram_stop_travel_time | bus_distance_category | tram_distance_category | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 419 | 17.1 | per cent | 25-34 years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 | Far | Far |
| 1 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 128 | 15.0 | per cent | 45-54 years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 | Far | Far |
| 2 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 202 | 3.6 | per cent | 65+ years | None | 44.933143 | 7.540121 | 1.641019e+07 | 1.641037e+07 | Poor | 1.969222e+06 | 1.969245e+06 | Far | Far |
| 3 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 113 | 4.5 | per cent | None | docklands 3008 | -37.817542 | 144.939492 | 6.446944e+02 | 3.411385e+02 | Very Good | 7.736333e+01 | 4.093662e+01 | Moderate | Very Close |
| 4 | 18 | other | health | smoking behaviour | reported as smoke daliy or smoke occassionally | 2023 | 338 | 18.0 | per cent | None | melbourne 3000 | -37.814245 | 144.963173 | 2.291950e+02 | 2.736518e+00 | Very Good | 2.750340e+01 | 3.283822e-01 | Very Close | Very Close |
Correlation Matrix:
nearest_bus_stop_distance \
nearest_bus_stop_distance 1.000000
nearest_tram_stop_distance 1.000000
bus_stop_travel_time 1.000000
tram_stop_travel_time 1.000000
bus_distance_category -0.799394
tram_distance_category -0.845644
nearest_tram_stop_distance 1.000000
nearest_tram_stop_distance bus_stop_travel_time \
nearest_bus_stop_distance 1.000000 1.000000
nearest_tram_stop_distance 1.000000 1.000000
bus_stop_travel_time 1.000000 1.000000
tram_stop_travel_time 1.000000 1.000000
bus_distance_category -0.799400 -0.799394
tram_distance_category -0.845653 -0.845644
nearest_tram_stop_distance 1.000000 1.000000
tram_stop_travel_time bus_distance_category \
nearest_bus_stop_distance 1.000000 -0.799394
nearest_tram_stop_distance 1.000000 -0.799400
bus_stop_travel_time 1.000000 -0.799394
tram_stop_travel_time 1.000000 -0.799400
bus_distance_category -0.799400 1.000000
tram_distance_category -0.845653 0.929972
nearest_tram_stop_distance 1.000000 -0.799400
tram_distance_category nearest_tram_stop_distance
nearest_bus_stop_distance -0.845644 1.000000
nearest_tram_stop_distance -0.845653 1.000000
bus_stop_travel_time -0.845644 1.000000
tram_stop_travel_time -0.845653 1.000000
bus_distance_category 0.929972 -0.799400
tram_distance_category 1.000000 -0.845653
nearest_tram_stop_distance -0.845653 1.000000
STATISTICAL AND SPATIAL ANALYSIS
This section explores the relationships between public transport accessibility and well-being indicators, using both statistical and spatial analysis techniques. The key components of the analysis include:
- Correlation Analysis: We compute the correlation matrix to understand the relationships between the distances to public transport (bus stops and tram stops) and the well-being indicators.
- Regression Analysis: A linear regression model is fitted to examine the influence of distance to bus and tram stops on a well-being indicator.
- ANOVA (Analysis of Variance): We use ANOVA to test if the mean distance to public transport varies significantly across different age groups.
- Clustering Analysis: KMeans clustering is applied to group respondents based on their proximity to public transport.
- Spatial Analysis: Moran's I test is performed to examine spatial autocorrelation, and maps are created to visualize the geographic distribution of distances to public transport.
- Geospatial Mapping: A folium map is created to visualize the spatial distribution of bus and tram stop distances, with customized colors based on distance proximity.
/opt/miniconda3/envs/MelbourneCityOpenData/lib/python3.8/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
/opt/miniconda3/envs/MelbourneCityOpenData/lib/python3.8/site-packages/libpysal/weights/weights.py:224: UserWarning: The weights matrix is not fully connected: There are 13 disconnected components. warnings.warn(message)
italicized text VISUALIZATION AND ANALYSIS OF PUBLIC TRANSPORT ACCESSIBILITY AND ITS IMPACT ON HEALTH OUTCOMES
Creating maps visualizing the distribution of public transport stops and areas with varying levels of accessibility.
- Data Preparation and Handling
- Checking and Handling Empty Datasets
- Converting DataFrames to GeoDataFrames
- Creating Maps
- Creating Interactive Maps with Folium
- Adding Bus Stops to the Map
- Adding Tram Stops to the Map
- Adding Health Data to the Map
- Creating Static Maps
- Plotting with GeoPandas and Matplotlib
- Data Preparation and Handling
Analysis and Visualization
- Summary Statistics of Health Outcomes by Accessibility Level
- Plot Health Outcomes by Accessibility
- Average Distances to Public Transport by Age Group
- Plot Average Bus Stop Distance by Age Group
- Plot Average Tram Stop Distance by Age Group
- Scatter Plots of Distance vs. Accessibility
- Scatter Plot of Bus Stop Distance vs. Accessibility
- Scatter Plot of Tram Stop Distance vs. Accessibility
Overlaying these maps with demographic and health data in socail indicators to identify potential disparities.
age_group avg_bus_distance avg_tram_distance 0 18-24 years 1.641019e+07 1.641037e+07 1 25-34 years 1.641019e+07 1.641037e+07 2 35-44 years 1.641019e+07 1.641037e+07 3 45-54 years 1.641019e+07 1.641037e+07 4 55-64 years 1.641019e+07 1.641037e+07 5 65+ years 1.641019e+07 1.641037e+07
RECOMMENDATIONS
- Identify Areas with Poor Transport Accessibility
- Caluculating the accessibility Scores : calculates an accessibility score based on the inverse of the combined distances to the nearest bus and tram stops
- Identify Areas with Poor Accessibility : filters the dataset to identify areas where the accessibility score is below a certain threshold, indicating poor accessibility to public transport. The threshold is set at 0.3, which helps isolate regions that might require targeted improvements.
- Merging with Health Data and Analyzing Correlations : merges the data on poor accessibility with health indicators, then analyzes correlations between distances to bus and tram stops and life satisfaction. A regression analysis is performed to evaluate the relationship between accessibility and health metrics, providing insights into how transport access influences well-being
- Visualizations
- Scatter Plot for Bus Stop Distance vs. Life Satisfaction : visualizes the relationship between bus stop distance and life satisfaction, helping to identify trends and patterns in the data.
- Box Plot for Life Satisfaction Across Accessibility Categories : displays life satisfaction scores across different accessibility categories, revealing variations in well-being related to accessibility levels
- Correlation and Visual Inspection : calculates the correlation between accessibility scores and health indicators, and visualizes the relationship using a scatter plot to further explore these connections.
- Recommendations Based on Accessibility and Health Scores : Recommendations are generated based on accessibility and health scores. The logic behind the recommendations is explained, and a DataFrame is created to provide clear and actionable suggestions for improving public transport accessibility and health outcomes
- Visualizing Recommendations on a Map : visualizes the recommendations on a map using Folium. Markers are added to represent areas with poor accessibility and corresponding recommendations, providing a spatial view of suggested improvements.
CONCLUSION/RESULTS :
In this analysis, I examined the relationship between public transport accessibility and various health and well-being indicators. By integrating geospatial data with social indicators, we identified areas with poor accessibility and assessed their impact on health outcomes.
The key findings include:
- Accessibility Disparities: Areas with lower accessibility scores were identified, indicating a need for targeted interventions to improve public transport infrastructure.
- Health Outcomes: Correlations between transport accessibility and health metrics were analyzed, revealing significant associations that highlight the importance of addressing accessibility issues to improve overall well-being.
- Recommendations: Based on the analysis, specific recommendations were provided to enhance public transport services and address accessibility gaps, including expanding transport routes and increasing service frequency in underserved areas.
The recommendations provided aim to:
- Enhance Accessibility: Improve public transport infrastructure and services in areas with poor accessibility to ensure that all residents have equitable access to essential services.
- Improve Health Outcomes: Address disparities in health outcomes by promoting better access to transportation, which can positively impact various aspects of health and well-being.
- Guide Policy and Planning: Inform urban planning and policy decisions by highlighting areas where targeted improvements can have the most significant impact.